The present invention relates to a method and apparatus for automatic classification and labeling of images of arterial trees, in particular coronary arteries.
Modern medical image techniques such as magnetic resonance imaging (MRI) and computerised tomography (CT) generate highly detailed images of a patient's internal anatomy. The imaging apparatuses produce so-called volume data sets comprising three-dimensional arrays of volume elements (voxels) where each voxel has a value indicating the value of some physical attribute of a corresponding small volume of the patient, where that attribute is measured by the imaging apparatus. For example, in CT this is the Hounsfield unit, which represents the attenuating ability of the patient's tissue to X-ray radiation.
Volume data sets require processing and manipulation by computer to render the data into a format which is useful and intelligible to medical personnel. For example, a two-dimensional image showing a slice through the volume of the data set can be produced. A further technique is that of segmenting, where the voxels corresponding to a particular organ or other anatomical structure are distinguished and separated from the surrounding voxels, for example by selecting contiguous voxels having values within a certain range. Images of the anatomical structure in isolation can then be displayed.
The coronary arterial tree is an example of such a structure which is frequently of medical interest. However, the arterial tree is a complex structure and its individual components may not be readily distinguishable on a displayed image. Therefore, it is useful if the image rendering process includes a classification procedure that can automatically identify and label the various parts. The labels can be displayed to the viewer to aid in procedures such as visual diagnosis, screening or training, and/or can be associated with the data set for use in automated diagnostics software and the like.
Currently techniques for coronary artery labeling rely on the technique of graph matching [1, 2]. However, this approach suffers from the problem that graph matching does not exploit the full knowledge about the coronary anatomy. Also, the cost functions used in previously proposed labeling methods do not consider the multivariate structure of the feature space. Therefore, there is a need for an alternative technique for automated arterial labeling.